Data Scientist II - Analysis, QC

About the opportunity Why This Role As a Data Scientist on the QC Hub team, you’ll be the analytical brain behind one of talabat’s fastest-growing verticals. You won’t just crunch numbers — you’ll partner directly with product and business leaders to shape strategy, design experiments that affect millions of users, and build the data foundations that power smarter decisions. This is a role for someone who loves turning messy, ambiguous business questions into clean, actionable analysis — and who gets energy from seeing their insights change how a team operates. What Success Looks Like - First 90 days: You’ve ramped up on your domain, built relationships with your product and business partners, understood the data landscape, and delivered your first actionable analysis. - By 6 months: You’re the go-to analytical partner for your domain. You’re independently designing and running experiments, and stakeholders regularly act on your recommendations. - By 12 months: Your work has measurably improved decision quality in your domain. You’ve built or refined data models that the team relies on daily, and you’re mentoring newer team members on analytical best practices. What You’ll Actually Do You’ll spend roughly: - 40% on deep analysis and experimentation — designing A/B tests, running multivariate experiments, doing deep dives into performance drivers, and turning findings into clear recommendations. - 30% on data modelling and quality — building and maintaining the data models that let us measure what matters, profiling source data, and ensuring data reliability. - 30% working with stakeholders — partnering with product and business managers to frame the right questions, set meaningful KPIs, and present insights that drive action. Day-to-day, you’ll: - Turn ambiguous business questions into structured analytical problems - Build and maintain dimensional data models in BigQuery - Design, execute, and interpret experiments (A/B and multivariate) - Create automated dashboards and reports that stakeholders actually use - Challenge assumptions with data — including your own - Collaborate with data engineers on logging and data pipeline quality You’ll Thrive Here If You… - Love being embedded with business teams, not siloed in a data team - Get satisfaction from changing how decisions are made, not just producing reports - Are comfortable with ambiguity — many of your best projects will start as vague questions - Care deeply about data quality and are willing to dig into source systems to understand what the data actually means - Communicate clearly with non-technical stakeholders This Might Not Be For You If You… - Want to build ML models full-time (this role is analytics and experimentation focused) - Prefer working independently without regular stakeholder interaction - Need clearly defined problems handed to you - Are more interested in tools and techniques than business impact What you need to be successful What You Bring Education Degree in a quantitative field (statistics, mathematics, economics, computer science, engineering, or similar) — or equivalent practical experience. A postgraduate degree is a plus but not required. Must-Haves: - Strong SQL skills — you can write complex queries with window functions, CTEs, and optimise for performance on large datasets - Reproducible analysis in Python or R — you write clean, well-structured analytical code, not one-off scripts - Experiment design expertise — you understand when to use A/B vs. multivariate tests, can calculate sample sizes, and know the pitfalls of statistical testing - Full analysis lifecycle experience — from problem framing through data auditing, analysis, interpretation, and presenting recommendations - Data modelling knowledge — you understand dimensional design and can build models that serve both ad-hoc analysis and automated reporting - Product analytics intuition — you’re familiar with metrics like conversion, engagement, and retention, and know how to measure product health - 3+ years in data science, analytics, or a related quantitative field Nice-to-Haves: - Experience with BigQuery and Google Cloud Platform - Data engineering skills (Airflow, dbt, or similar pipeline tools) - Experience with ML frameworks (Scikit-learn, XGBoost, LightGBM) - Familiarity with modern data tools and AI-assisted analysis workflows - Experience in an online consumer product or marketplace environment Who we are Since launching in Kuwait in 2004, talabat has become the region’s leading on-demand delivery app, serving millions of customers across eight countries. Our Quick Commerce (QC) Hub powers the grocery and convenience delivery experience — getting everyday essentials to customers’ doors in minutes, not hours. Behind every delivery is data. Our QC data team works at a scale most data scientists only read about: millions of daily transactions, thousands of partners, and decisions that directly shape how people across the Middle East get their grocerie

Education: Degree in a quantitative field (statistics, mathematics, economics, computer science, engineering, or similar) or equivalent practical experience. Postgraduate degree is a plus but not required. Must-haves: • Strong SQL skills with complex queries, window functions, CTEs, and performance optimization on large datasets • Reproducible analysis in Python or R with clean, well-structured code • Experiment design expertise (A/B vs. multivariate, sample size calculation, understanding statistical pitfalls) • Full analysis lifecycle experience from framing to auditing, interpretation, and presenting recommendations • Data modelling knowledge with dimensional design for ad-hoc analysis and automated reporting • Product analytics intuition (metrics like conversion, engagement, retention) and ability to measure product health • 3+ years in data science, analytics, or a related quantitative field Nice-to-haves: • Experience with BigQuery and Google Cloud Platform • Data engineering skills (Airflow, dbt, or similar) • Experience with ML frameworks (Scikit-learn, XGBoost, LightGBM) • Familiarity with modern data tools and AI-assisted analysis workflows • Experience in an online consumer product or marketplace environment

What You'll Actually Do: • Spend ~40% on deep analysis and experimentation (designing A/B tests, multivariate experiments, deep dives, turning findings into clear recommendations) • ~30% on data modelling and quality (build/maintain data models, profile source data, ensure data reliability) • ~30% working with stakeholders (frame questions, set meaningful KPIs, present insights to drive action) Day-to-day: • Turn ambiguous business questions into structured analytical problems • Build and maintain dimensional data models in BigQuery • Design, execute, and interpret experiments (A/B and multivariate) • Create automated dashboards and reports used by stakeholders • Challenge assumptions with data (including your own) • Collaborate with data engineers on logging and data pipeline quality